AEGANAuth: Autoencoder GAN-Based Continuous Authentication With Conditional Variational Autoencoder Generative Adversarial Network
Yantao Li, Caike Ouyang, Hongyu Huang · IEEE Internet of Things Journal · 2024
In recent years, sensor-based continuous authentication on mobile devices has proven highly effective in safeguarding personal information. However, these proposed approaches often require the utilization of both legitimate user and imposters’ data for training authentication models, which is time-consuming and ineffective. In this paper, we present AEGANAuth, a lightweight and effective AutoEncoder GAN-based continuous Authentication system for mobile devices using conditional variational AutoEncoder Generative Adversarial Network. AEGANAuth uses a Conditional Variational AutoEncoder Generative Adversarial Network (CVAEGAN) for data augmentation and utilizes an AutoEncoder Generative Adversarial Network (AEGAN) for user data reconstruction. During the enrollment phase, AEGANAuth employs the accelerometer and gyroscope sensors embedded on mobile devices to implicitly collect user behavioral patterns. Using the normalized sensor data, AEGANAuth selects legitimate user data to train CVAEGAN, which consists of a variational encoder, a conditional generator, a discriminator, and a classifier, for AEGAN training data augmentation. Based on the augmented legitimate user data, AEGAN, comprising an encoder, a decoder, and a discriminator, is trained for user data reconstruction. In the authentication phase, when a user operates the mobile device, AEGANAuth collects and normalizes the current user’s data, and then employs the trained AEGAN to reconstruct this user’s data. The reconstruction error is then computed by comparing the reconstructed data to the normalized data. Finally, AEGANAuth with AEGAN compares the reconstruction error to a predetermined authentication threshold for user authentication. We evaluate the performance of AEGANAuth on our dataset, and the experimental results demonstrate an average equal error rate (EER) of 2.13% and an average accuracy of 97.85% on 10 imposters.